AlphaMat: a material informatics hub connecting data, features, models and applications

The development of modern civil industry, energy and information technology is inseparable from the rapid explorations of new materials. However, only a small fraction of materials being experimentally/computationally studied in a vast chemical space. Artificial intelligence (AI) is promising to add...

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Published innpj computational materials Vol. 9; no. 1; pp. 130 - 9
Main Authors Wang, Zhilong, Chen, An, Tao, Kehao, Cai, Junfei, Han, Yanqiang, Gao, Jing, Ye, Simin, Wang, Shiwei, Ali, Imran, Li, Jinjin
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 26.07.2023
Nature Publishing Group
Nature Portfolio
Subjects
639/301/1034/1035
639/301/1034/1037
Artificial intelligence
Bulk modulus
Characterization and Evaluation of Materials
Chemistry and Materials Science
Computational Intelligence
Computer science
Conductivity
Data processing
Design
Efficiency
Energy
Energy gap
Experiments
Free energy
Heat of formation
Informatics
Information technology
Interdisciplinary subjects
Ion currents
Machine learning
Materials information
Materials Science
Mathematical and Computational Engineering
Mathematical and Computational Physics
Mathematical Modeling and Industrial Mathematics
Proprietary
Research methodology
Simulation
Theoretical
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Abstract The development of modern civil industry, energy and information technology is inseparable from the rapid explorations of new materials. However, only a small fraction of materials being experimentally/computationally studied in a vast chemical space. Artificial intelligence (AI) is promising to address this gap, but faces many challenges, such as data scarcity and inaccurate material descriptors. Here, we develop an AI platform, AlphaMat, that can complete data preprocessing and downstream AI models. With high efficiency and accuracy, AlphaMat exhibits strong powers to model typical 12 material attributes (formation energy, band gap, ionic conductivity, magnetism, bulk modulus, etc.). AlphaMat’s capabilities are further demonstrated to discover thousands of new materials for use in specific domains. AlphaMat does not require users to have strong programming experience, and its effective use will facilitate the development of materials informatics, which is of great significance for the implementation of AI for Science (AI4S).
AbstractList Abstract The development of modern civil industry, energy and information technology is inseparable from the rapid explorations of new materials. However, only a small fraction of materials being experimentally/computationally studied in a vast chemical space. Artificial intelligence (AI) is promising to address this gap, but faces many challenges, such as data scarcity and inaccurate material descriptors. Here, we develop an AI platform, AlphaMat, that can complete data preprocessing and downstream AI models. With high efficiency and accuracy, AlphaMat exhibits strong powers to model typical 12 material attributes (formation energy, band gap, ionic conductivity, magnetism, bulk modulus, etc.). AlphaMat’s capabilities are further demonstrated to discover thousands of new materials for use in specific domains. AlphaMat does not require users to have strong programming experience, and its effective use will facilitate the development of materials informatics, which is of great significance for the implementation of AI for Science (AI4S).
The development of modern civil industry, energy and information technology is inseparable from the rapid explorations of new materials. However, only a small fraction of materials being experimentally/computationally studied in a vast chemical space. Artificial intelligence (AI) is promising to address this gap, but faces many challenges, such as data scarcity and inaccurate material descriptors. Here, we develop an AI platform, AlphaMat, that can complete data preprocessing and downstream AI models. With high efficiency and accuracy, AlphaMat exhibits strong powers to model typical 12 material attributes (formation energy, band gap, ionic conductivity, magnetism, bulk modulus, etc.). AlphaMat’s capabilities are further demonstrated to discover thousands of new materials for use in specific domains. AlphaMat does not require users to have strong programming experience, and its effective use will facilitate the development of materials informatics, which is of great significance for the implementation of AI for Science (AI4S).
ArticleNumber 130
Author Cai, Junfei
Tao, Kehao
Wang, Zhilong
Wang, Shiwei
Han, Yanqiang
Gao, Jing
Ali, Imran
Chen, An
Ye, Simin
Li, Jinjin
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  surname: Li
  fullname: Li, Jinjin
  email: lijinjin@sjtu.edu.cn
  organization: National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University
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Cites_doi 10.1021/acsami.1c10749
10.1002/wcms.1411
10.1016/j.joule.2021.10.010
10.1038/s41586-022-04599-z
10.1016/j.cossms.2014.02.003
10.1063/1.4812323
10.1038/s41560-020-00759-5
10.1021/acs.jpclett.8b00124
10.1557/mrs.2018.208
10.1038/s41578-021-00376-y
10.1016/j.cpc.2021.108171
10.1038/s41578-021-00345-5
10.1039/D1EE01170G
10.1039/D1EE00442E
10.1021/acsami.0c15728
10.1038/s41586-018-0337-2
10.1016/j.commatsci.2018.05.018
10.1038/sdata.2016.134
10.1038/s41563-021-01015-1
10.1016/j.scib.2021.06.011
10.1103/PhysRev.136.B864
10.1021/acsenergylett.9b00549
10.1016/j.cpc.2012.05.008
10.1088/1674-4926/43/4/042101
10.1021/acs.jpclett.1c03526
10.1146/annurev-matsci-070218-010015
10.1103/PhysRevB.89.094104
10.1016/j.cpc.2021.108033
10.1016/j.jechem.2022.03.052
10.1038/s41578-020-00255-y
10.1016/j.joule.2018.05.009
10.1021/acs.jctc.9b00322
10.1016/j.commatsci.2020.109544
10.1016/j.commatsci.2012.10.028
10.1002/inf2.12094
10.1038/s41524-021-00596-4
10.1038/sdata.2018.65
10.1038/s41524-020-00406-3
10.1063/1.4946894
10.1021/acsaem.0c00230
10.1088/0953-8984/21/39/395502
10.1021/acsenergylett.1c00332
10.1038/s41524-020-00360-0
10.1038/s41467-022-29904-2
10.1038/npjcompumats.2015.10
10.1016/j.commatsci.2022.111187
10.1016/j.nanoen.2020.105665
10.1038/s41563-021-01013-3
10.1038/s41597-020-00723-8
10.1038/s42254-022-00423-9
10.1016/j.commatsci.2020.110064
10.1016/j.physrep.2021.08.002
10.1021/acs.chemmater.9b04066
10.1103/PhysRevB.54.11169
10.1038/s41560-021-00827-4
10.1002/adfm.202003087
10.1038/s41524-019-0173-4
10.1016/j.nanoen.2021.106337
10.1038/s41524-022-00935-z
10.1038/s41597-019-0097-3
10.1103/PhysRev.140.A1133
10.1038/s41597-020-0474-y
10.1103/PhysRevB.82.094116
10.1021/acs.jpcc.7b00810
10.1021/jacs.1c05441
10.1145/2939672.2939785
10.1007/s11837-014-0888-0
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References de Pablo (CR23) 2019; 5
Wang, Zhang, Li (CR49) 2021; 81
Kim (CR18) 2020; 7
Zheng (CR11) 2019; 9
Zhang (CR71) 2020; 30
Zhang, Wang, Ren, Liu, Li (CR30) 2021; 35
Borlido (CR59) 2019; 15
Wang (CR51) 2021; 89
Huang (CR12) 2022; 43
He (CR54) 2021; 6
Wang, Lv, Zhu, Ma (CR9) 2010; 82
Morgan, Jacobs (CR43) 2020; 50
de Pablo, Jones, Kovacs, Ozolins, Ramirez (CR19) 2014; 18
Meredig (CR48) 2014; 89
Giannozzi (CR8) 2009; 21
Petretto (CR69) 2018; 5
Kohn, Sham (CR15) 1965; 140
Sbailò, Fekete, Ghiringhelli, Scheffler (CR42) 2022; 8
Jain (CR16) 2013; 1
Thompson (CR7) 2022; 271
CR47
CR46
Draxl, Scheffler (CR22) 2018; 43
Lu (CR36) 2022; 604
Zhou, Dong, Hsieh, Goncharov, Chen (CR64) 2022; 4
Jacobs (CR41) 2020; 176
Kresse, Furthmüller (CR6) 1996; 54
Wang, Schwartz, Gim, Hovden, Mi (CR52) 2019; 4
Wang, Lv, Zhu, Ma (CR10) 2012; 183
Zhang, Wang, Cai, Wu, Li (CR66) 2021; 13
Lyu, Moore, Liu, Yu, Wu (CR32) 2021; 143
Zhang (CR53) 2020; 3
Petousis (CR70) 2017; 4
Borlido (CR58) 2020; 6
Louie, Chan, da Jornada, Li, Qiu (CR3) 2021; 20
Wang, Han, Cai, Chen, Li (CR28) 2022; 71
Ong (CR44) 2013; 68
Chen (CR57) 2021; 6
Kim, Drakopoulos, Ronca, Minnich (CR63) 2022; 13
Hammes-Schiffer, Galli (CR5) 2021; 6
Zhou (CR67) 2019; 6
Han (CR27) 2021; 934
Jiang (CR31) 2021; 5
He (CR17) 2020; 7
Agrawal, Choudhary (CR24) 2016; 4
Pedregosa (CR45) 2011; 12
Wang (CR60) 2021; 39
Kirklin (CR21) 2015; 1
Chen, Zhang, Zhou (CR26) 2020; 2
Dunn, Wang, Ganose, Dopp, Jain (CR40) 2020; 6
Balaish (CR56) 2021; 6
Wang, Xu, Liu, Tang, Geng (CR13) 2021; 267
Zhuo, Mansouri Tehrani, Brgoch (CR68) 2018; 9
Miyake, Saeki (CR34) 2021; 12
Zhao (CR38) 2021; 66
Sun, Zhao (CR61) 2017; 121
Hohenberg, Kohn (CR14) 1964; 136
Marzari, Ferretti, Wolverton (CR2) 2021; 20
Yu (CR73) 2020; 12
Cai, Wang, Wu, Han, Li (CR50) 2021; 42
Butler, Davies, Cartwright, Isayev, Walsh (CR25) 2018; 559
CR20
Zhu (CR72) 2021; 14
Ward (CR39) 2018; 152
Daehn (CR1) 2021; 7
Wang (CR37) 2021; 186
Correa-Baena (CR4) 2018; 2
Wang (CR55) 2022; 204
Wang, Cai, Wang, Wu, Li (CR33) 2021; 7
Wang (CR65) 2023; 59
Zou (CR29) 2021; 14
Batra, Song, Ramprasad (CR35) 2021; 6
Chakraborty (CR62) 2020; 32
Y Han (1086_CR27) 2021; 934
X-G Zhao (1086_CR38) 2021; 66
Q Zheng (1086_CR11) 2019; 9
Y Wang (1086_CR10) 2012; 183
M Balaish (1086_CR56) 2021; 6
Z Wang (1086_CR33) 2021; 7
F Pedregosa (1086_CR45) 2011; 12
X Zou (1086_CR29) 2021; 14
J Yu (1086_CR73) 2020; 12
R Jacobs (1086_CR41) 2020; 176
B Meredig (1086_CR48) 2014; 89
A Chakraborty (1086_CR62) 2020; 32
B He (1086_CR17) 2020; 7
Z Wang (1086_CR55) 2022; 204
A Chen (1086_CR26) 2020; 2
P Giannozzi (1086_CR8) 2009; 21
JJ de Pablo (1086_CR23) 2019; 5
P Borlido (1086_CR58) 2020; 6
Z Wang (1086_CR51) 2021; 89
Y Zhuo (1086_CR68) 2018; 9
A Jain (1086_CR16) 2013; 1
1086_CR20
H Zhang (1086_CR66) 2021; 13
G Petretto (1086_CR69) 2018; 5
G Kresse (1086_CR6) 1996; 54
J Cai (1086_CR50) 2021; 42
P Borlido (1086_CR59) 2019; 15
Y-T Chen (1086_CR57) 2021; 6
Y Zhou (1086_CR64) 2022; 4
Z Wang (1086_CR65) 2023; 59
Y Wang (1086_CR9) 2010; 82
Y Zhang (1086_CR53) 2020; 3
P Hohenberg (1086_CR14) 1964; 136
Y Miyake (1086_CR34) 2021; 12
M Huang (1086_CR12) 2022; 43
R Lyu (1086_CR32) 2021; 143
T Kim (1086_CR63) 2022; 13
L Zhang (1086_CR71) 2020; 30
S Hammes-Schiffer (1086_CR5) 2021; 6
S Kim (1086_CR18) 2020; 7
AP Thompson (1086_CR7) 2022; 271
H Lu (1086_CR36) 2022; 604
L Ward (1086_CR39) 2018; 152
Z Wang (1086_CR49) 2021; 81
B Jiang (1086_CR31) 2021; 5
H Sun (1086_CR61) 2017; 121
C Draxl (1086_CR22) 2018; 43
Z Wang (1086_CR60) 2021; 39
JJ de Pablo (1086_CR19) 2014; 18
G Wang (1086_CR37) 2021; 186
V Wang (1086_CR13) 2021; 267
R Batra (1086_CR35) 2021; 6
J-P Correa-Baena (1086_CR4) 2018; 2
Z Wang (1086_CR28) 2022; 71
SG Louie (1086_CR3) 2021; 20
A Agrawal (1086_CR24) 2016; 4
W Kohn (1086_CR15) 1965; 140
I Petousis (1086_CR70) 2017; 4
Y Wang (1086_CR52) 2019; 4
X He (1086_CR54) 2021; 6
J Zhou (1086_CR67) 2019; 6
K Daehn (1086_CR1) 2021; 7
H Zhang (1086_CR30) 2021; 35
L Sbailò (1086_CR42) 2022; 8
N Marzari (1086_CR2) 2021; 20
A Dunn (1086_CR40) 2020; 6
KT Butler (1086_CR25) 2018; 559
T Zhu (1086_CR72) 2021; 14
SP Ong (1086_CR44) 2013; 68
1086_CR47
S Kirklin (1086_CR21) 2015; 1
1086_CR46
D Morgan (1086_CR43) 2020; 50
References_xml – volume: 13
  start-page: 53388
  year: 2021
  end-page: 53397
  ident: CR66
  article-title: Machine-learning-enabled tricks of the trade for rapid host material discovery in Li–S battery
  publication-title: ACS Appl. Mater. Interfaces
  doi: 10.1021/acsami.1c10749
– volume: 9
  start-page: e1411
  year: 2019
  ident: CR11
  article-title: Ab initio nonadiabatic molecular dynamics investigations on the excited carriers in condensed matter systems
  publication-title: WIREs Comput. Mol. Sci.
  doi: 10.1002/wcms.1411
– volume: 5
  start-page: 3187
  year: 2021
  end-page: 3203
  ident: CR31
  article-title: Bayesian learning for rapid prediction of lithium-ion battery-cycling protocols
  publication-title: Joule
  doi: 10.1016/j.joule.2021.10.010
– volume: 604
  start-page: 662
  year: 2022
  end-page: 667
  ident: CR36
  article-title: Machine learning-aided engineering of hydrolases for PET depolymerization
  publication-title: Nature
  doi: 10.1038/s41586-022-04599-z
– volume: 18
  start-page: 99
  year: 2014
  end-page: 117
  ident: CR19
  article-title: The Materials Genome Initiative, the interplay of experiment, theory and computation
  publication-title: Curr. Opin. Solid State Mater. Sci.
  doi: 10.1016/j.cossms.2014.02.003
– volume: 59
  start-page: 102781
  year: 2023
  ident: CR65
  article-title: IonML: A physically inspired machine learning platform to directed design superionic conductors
  publication-title: Energy Stor. Mater.
– volume: 39
  start-page: 45
  year: 2021
  end-page: 53
  ident: CR60
  article-title: Deep learning for ultra-fast and high precision screening of energy materials
  publication-title: Energy Stor. Mater.
– volume: 1
  start-page: 011002
  year: 2013
  ident: CR16
  article-title: The Materials Project: a materials genome approach to accelerating materials innovation
  publication-title: APL Mater.
  doi: 10.1063/1.4812323
– volume: 42
  start-page: 277
  year: 2021
  end-page: 285
  ident: CR50
  article-title: A machine learning shortcut for screening the spinel structures of Mg/Zn ion battery cathodes with a high conductivity and rapid ion kinetics
  publication-title: Energy Stor. Mater.
– volume: 6
  start-page: 227
  year: 2021
  end-page: 239
  ident: CR56
  article-title: Processing thin but robust electrolytes for solid-state batteries
  publication-title: Nat. Energy
  doi: 10.1038/s41560-020-00759-5
– volume: 9
  start-page: 1668
  year: 2018
  end-page: 1673
  ident: CR68
  article-title: Predicting the band gaps of inorganic solids by machine learning
  publication-title: J. Phys. Chem. Lett.
  doi: 10.1021/acs.jpclett.8b00124
– volume: 43
  start-page: 676
  year: 2018
  end-page: 682
  ident: CR22
  article-title: NOMAD: The FAIR concept for big data-driven materials science
  publication-title: MRS Bull.
  doi: 10.1557/mrs.2018.208
– volume: 7
  start-page: 275
  year: 2021
  end-page: 294
  ident: CR1
  article-title: Innovations to decarbonize materials industries
  publication-title: Nat. Rev. Mater.
  doi: 10.1038/s41578-021-00376-y
– volume: 271
  start-page: 108171
  year: 2022
  ident: CR7
  article-title: LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales
  publication-title: Comput. Phys. Commun.
  doi: 10.1016/j.cpc.2021.108171
– volume: 6
  start-page: 1036
  year: 2021
  end-page: 1052
  ident: CR54
  article-title: The passivity of lithium electrodes in liquid electrolytes for secondary batteries
  publication-title: Nat. Rev. Mater.
  doi: 10.1038/s41578-021-00345-5
– volume: 14
  start-page: 3965
  year: 2021
  end-page: 3975
  ident: CR29
  article-title: Machine learning analysis and prediction models of alkaline anion exchange membranes for fuel cells
  publication-title: Energy Environ. Sci.
  doi: 10.1039/D1EE01170G
– volume: 14
  start-page: 3559
  year: 2021
  end-page: 3566
  ident: CR72
  article-title: Charting lattice thermal conductivity for inorganic crystals and discovering rare earth chalcogenides for thermoelectrics
  publication-title: Energy Environ. Sci.
  doi: 10.1039/D1EE00442E
– volume: 12
  start-page: 45023
  year: 2020
  end-page: 45035
  ident: CR73
  article-title: Finding optimal mid-infrared nonlinear optical materials in germanates by first-principles high-throughput screening and experimental verification
  publication-title: ACS Appl. Mater. Interfaces
  doi: 10.1021/acsami.0c15728
– volume: 559
  start-page: 547
  year: 2018
  end-page: 555
  ident: CR25
  article-title: Machine learning for molecular and materials science
  publication-title: Nature
  doi: 10.1038/s41586-018-0337-2
– volume: 152
  start-page: 60
  year: 2018
  end-page: 69
  ident: CR39
  article-title: Matminer: An open source toolkit for materials data mining
  publication-title: Comput. Mater. Sci.
  doi: 10.1016/j.commatsci.2018.05.018
– ident: CR46
– volume: 4
  year: 2017
  ident: CR70
  article-title: High-throughput screening of inorganic compounds for the discovery of novel dielectric and optical materials
  publication-title: Sci. Data
  doi: 10.1038/sdata.2016.134
– volume: 20
  start-page: 728
  year: 2021
  end-page: 735
  ident: CR3
  article-title: Discovering and understanding materials through computation
  publication-title: Nat. Mater.
  doi: 10.1038/s41563-021-01015-1
– volume: 66
  start-page: 1973
  year: 2021
  end-page: 1985
  ident: CR38
  article-title: JAMIP: an artificial-intelligence aided data-driven infrastructure for computational materials informatics
  publication-title: Sci. Bull.
  doi: 10.1016/j.scib.2021.06.011
– volume: 12
  start-page: 2825
  year: 2011
  end-page: 2830
  ident: CR45
  article-title: Scikit-learn: machine learning in python
  publication-title: J. Mach. Learn. Res.
– volume: 136
  start-page: B864
  year: 1964
  end-page: B871
  ident: CR14
  article-title: Inhomogeneous electron gas
  publication-title: Phys. Rev.
  doi: 10.1103/PhysRev.136.B864
– volume: 4
  start-page: 1541
  year: 2019
  end-page: 1548
  ident: CR52
  article-title: Stable unassisted solar water splitting on semiconductor photocathodes protected by multifunctional GaN nanostructures
  publication-title: ACS Energy Lett.
  doi: 10.1021/acsenergylett.9b00549
– volume: 183
  start-page: 2063
  year: 2012
  end-page: 2070
  ident: CR10
  article-title: CALYPSO: A method for crystal structure prediction
  publication-title: Comput. Phys. Commun.
  doi: 10.1016/j.cpc.2012.05.008
– volume: 43
  start-page: 042101
  year: 2022
  ident: CR12
  article-title: DASP: defect and dopant ab-initio simulation package
  publication-title: J. Semicond.
  doi: 10.1088/1674-4926/43/4/042101
– volume: 12
  start-page: 12391
  year: 2021
  end-page: 12401
  ident: CR34
  article-title: Machine learning-assisted development of organic solar cell materials: issues, analyses, and outlooks
  publication-title: J. Phys. Chem. Lett.
  doi: 10.1021/acs.jpclett.1c03526
– volume: 50
  start-page: 71
  year: 2020
  end-page: 103
  ident: CR43
  article-title: Opportunities and Challenges for Machine Learning in Materials Science
  publication-title: Annu. Rev. Mater. Res.
  doi: 10.1146/annurev-matsci-070218-010015
– volume: 89
  start-page: 094104
  year: 2014
  ident: CR48
  article-title: Combinatorial screening for new materials in unconstrained composition space with machine learning
  publication-title: Phys. Rev. B
  doi: 10.1103/PhysRevB.89.094104
– volume: 267
  start-page: 108033
  year: 2021
  ident: CR13
  article-title: VASPKIT: A user-friendly interface facilitating high-throughput computing and analysis using VASP code
  publication-title: Comput. Phys. Commun.
  doi: 10.1016/j.cpc.2021.108033
– volume: 71
  start-page: 56
  year: 2022
  end-page: 62
  ident: CR28
  article-title: Vision for energy material design: a roadmap for integrated data-driven modeling
  publication-title: J. Energy Chem.
  doi: 10.1016/j.jechem.2022.03.052
– volume: 6
  start-page: 655
  year: 2021
  end-page: 678
  ident: CR35
  article-title: Emerging materials intelligence ecosystems propelled by machine learning
  publication-title: Nat. Rev. Mater.
  doi: 10.1038/s41578-020-00255-y
– volume: 2
  start-page: 1410
  year: 2018
  end-page: 1420
  ident: CR4
  article-title: Accelerating materials development via automation, machine learning, and high-performance computing
  publication-title: Joule
  doi: 10.1016/j.joule.2018.05.009
– volume: 15
  start-page: 5069
  year: 2019
  end-page: 5079
  ident: CR59
  article-title: Large-scale benchmark of exchange–correlation functionals for the determination of electronic band gaps of solids
  publication-title: J. Chem. Theory Comput.
  doi: 10.1021/acs.jctc.9b00322
– volume: 176
  start-page: 109544
  year: 2020
  ident: CR41
  article-title: The Materials Simulation Toolkit for Machine learning (MAST-ML): An automated open source toolkit to accelerate data-driven materials research
  publication-title: Comput. Mater. Sci.
  doi: 10.1016/j.commatsci.2020.109544
– volume: 68
  start-page: 314
  year: 2013
  end-page: 319
  ident: CR44
  article-title: Python materials genomics (pymatgen): a robust, open-source python library for materials analysis
  publication-title: Comput. Mater. Sci.
  doi: 10.1016/j.commatsci.2012.10.028
– volume: 2
  start-page: 553
  year: 2020
  end-page: 576
  ident: CR26
  article-title: Machine learning: Accelerating materials development for energy storage and conversion
  publication-title: InfoMat
  doi: 10.1002/inf2.12094
– ident: CR47
– volume: 7
  start-page: 128
  year: 2021
  ident: CR33
  article-title: Unsupervised discovery of thin-film photovoltaic materials from unlabeled data
  publication-title: npj Comput. Mater.
  doi: 10.1038/s41524-021-00596-4
– volume: 5
  year: 2018
  ident: CR69
  article-title: High-throughput density-functional perturbation theory phonons for inorganic materials
  publication-title: Sci. Data
  doi: 10.1038/sdata.2018.65
– volume: 6
  start-page: 138
  year: 2020
  ident: CR40
  article-title: Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm
  publication-title: npj Comput. Mater.
  doi: 10.1038/s41524-020-00406-3
– volume: 4
  start-page: 053208
  year: 2016
  ident: CR24
  article-title: Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science
  publication-title: APL Mater.
  doi: 10.1063/1.4946894
– volume: 3
  start-page: 3755
  year: 2020
  end-page: 3769
  ident: CR53
  article-title: Synthesis and characterization of spinel cobaltite (Co O ) thin films for function as hole transport materials in organometallic halide perovskite solar cells
  publication-title: ACS Appl. Energy Mater.
  doi: 10.1021/acsaem.0c00230
– volume: 21
  start-page: 395502
  year: 2009
  ident: CR8
  article-title: QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials
  publication-title: J. Phys. Condens. Matter
  doi: 10.1088/0953-8984/21/39/395502
– volume: 6
  start-page: 1639
  year: 2021
  end-page: 1648
  ident: CR57
  article-title: Fabrication of high-quality thin solid-state electrolyte films assisted by machine learning
  publication-title: ACS Energy Lett.
  doi: 10.1021/acsenergylett.1c00332
– volume: 6
  start-page: 96
  year: 2020
  ident: CR58
  article-title: Exchange-correlation functionals for band gaps of solids: benchmark, reparametrization and machine learning
  publication-title: npj Comput. Mater.
  doi: 10.1038/s41524-020-00360-0
– volume: 13
  year: 2022
  ident: CR63
  article-title: Origin of high thermal conductivity in disentangled ultra-high molecular weight polyethylene films: ballistic phonons within enlarged crystals
  publication-title: Nat. Commun.
  doi: 10.1038/s41467-022-29904-2
– volume: 1
  start-page: 15010
  year: 2015
  ident: CR21
  article-title: The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
  publication-title: npj Comput. Mater.
  doi: 10.1038/npjcompumats.2015.10
– volume: 35
  start-page: 88
  year: 2021
  end-page: 98
  ident: CR30
  article-title: Ultra-fast and accurate binding energy prediction of shuttle effect-suppressive sulfur hosts for lithium-sulfur batteries using machine learning
  publication-title: Energy Stor. Mater.
– volume: 204
  start-page: 111187
  year: 2022
  ident: CR55
  article-title: Computational screening of spinel structure cathodes for Li-ion battery with low expansion and rapid ion kinetics
  publication-title: Comput. Mater. Sci.
  doi: 10.1016/j.commatsci.2022.111187
– volume: 81
  start-page: 105665
  year: 2021
  ident: CR49
  article-title: Accelerated discovery of stable spinels in energy systems via machine learning
  publication-title: Nano Energy
  doi: 10.1016/j.nanoen.2020.105665
– volume: 20
  start-page: 736
  year: 2021
  end-page: 749
  ident: CR2
  article-title: Electronic-structure methods for materials design
  publication-title: Nat. Mater.
  doi: 10.1038/s41563-021-01013-3
– volume: 7
  year: 2020
  ident: CR18
  article-title: A band-gap database for semiconducting inorganic materials calculated with hybrid functional
  publication-title: Sci. Data
  doi: 10.1038/s41597-020-00723-8
– volume: 4
  start-page: 319
  year: 2022
  end-page: 335
  ident: CR64
  article-title: Thermal conductivity of materials under pressure
  publication-title: Nat. Rev. Phys.
  doi: 10.1038/s42254-022-00423-9
– volume: 186
  start-page: 110064
  year: 2021
  ident: CR37
  article-title: ALKEMIE: An intelligent computational platform for accelerating materials discovery and design
  publication-title: Comput. Mater. Sci.
  doi: 10.1016/j.commatsci.2020.110064
– volume: 934
  start-page: 1
  year: 2021
  end-page: 71
  ident: CR27
  article-title: Machine learning accelerates quantum mechanics predictions of molecular crystals
  publication-title: Phys. Rep.
  doi: 10.1016/j.physrep.2021.08.002
– volume: 32
  start-page: 915
  year: 2020
  end-page: 952
  ident: CR62
  article-title: Layered cathode materials for lithium-ion batteries: review of computational studies on LiNi Co Mn O and LiNi Co Al O
  publication-title: Chem. Mater.
  doi: 10.1021/acs.chemmater.9b04066
– volume: 54
  start-page: 11169
  year: 1996
  end-page: 11186
  ident: CR6
  article-title: Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set
  publication-title: Phys. Rev. B
  doi: 10.1103/PhysRevB.54.11169
– volume: 6
  start-page: 700
  year: 2021
  end-page: 705
  ident: CR5
  article-title: Integration of theory and experiment in the modelling of heterogeneous electrocatalysis
  publication-title: Nat. Energy
  doi: 10.1038/s41560-021-00827-4
– volume: 30
  start-page: 2003087
  year: 2020
  ident: CR71
  article-title: A database of ionic transport characteristics for over 29 000 inorganic compounds
  publication-title: Adv. Funct. Mater.
  doi: 10.1002/adfm.202003087
– volume: 5
  start-page: 41
  year: 2019
  ident: CR23
  article-title: New frontiers for the materials genome initiative
  publication-title: npj Comput. Mater.
  doi: 10.1038/s41524-019-0173-4
– volume: 89
  start-page: 106337
  year: 2021
  ident: CR51
  article-title: Harnessing artificial intelligence to holistic design and identification for solid electrolytes
  publication-title: Nano Energy
  doi: 10.1016/j.nanoen.2021.106337
– volume: 8
  start-page: 250
  year: 2022
  ident: CR42
  article-title: The NOMAD Artificial-Intelligence Toolkit: turning materials-science data into knowledge and understanding
  publication-title: npj Comput. Mater.
  doi: 10.1038/s41524-022-00935-z
– volume: 6
  year: 2019
  ident: CR67
  article-title: 2DMatPedia, an open computational database of two-dimensional materials from top-down and bottom-up approaches
  publication-title: Sci. Data
  doi: 10.1038/s41597-019-0097-3
– volume: 140
  start-page: A1133
  year: 1965
  end-page: A1138
  ident: CR15
  article-title: Self-consistent equations including exchange and correlation effects
  publication-title: Phys. Rev.
  doi: 10.1103/PhysRev.140.A1133
– volume: 7
  year: 2020
  ident: CR17
  article-title: High-throughput screening platform for solid electrolytes combining hierarchical ion-transport prediction algorithms
  publication-title: Sci. Data
  doi: 10.1038/s41597-020-0474-y
– volume: 82
  start-page: 094116
  year: 2010
  ident: CR9
  article-title: Crystal structure prediction via particle-swarm optimization
  publication-title: Phys. Rev. B
  doi: 10.1103/PhysRevB.82.094116
– volume: 121
  start-page: 6002
  year: 2017
  end-page: 6010
  ident: CR61
  article-title: Electronic structure and comparative properties of LiNi Mn Co O cathode materials
  publication-title: J. Phys. Chem. C.
  doi: 10.1021/acs.jpcc.7b00810
– ident: CR20
– volume: 143
  start-page: 12766
  year: 2021
  end-page: 12776
  ident: CR32
  article-title: Predictive design model for low-dimensional organic–inorganic halide perovskites assisted by machine learning
  publication-title: J. Am. Chem. Soc.
  doi: 10.1021/jacs.1c05441
– volume: 6
  start-page: 1639
  year: 2021
  ident: 1086_CR57
  publication-title: ACS Energy Lett.
  doi: 10.1021/acsenergylett.1c00332
– volume: 68
  start-page: 314
  year: 2013
  ident: 1086_CR44
  publication-title: Comput. Mater. Sci.
  doi: 10.1016/j.commatsci.2012.10.028
– volume: 14
  start-page: 3559
  year: 2021
  ident: 1086_CR72
  publication-title: Energy Environ. Sci.
  doi: 10.1039/D1EE00442E
– volume: 42
  start-page: 277
  year: 2021
  ident: 1086_CR50
  publication-title: Energy Stor. Mater.
– volume: 82
  start-page: 094116
  year: 2010
  ident: 1086_CR9
  publication-title: Phys. Rev. B
  doi: 10.1103/PhysRevB.82.094116
– volume: 136
  start-page: B864
  year: 1964
  ident: 1086_CR14
  publication-title: Phys. Rev.
  doi: 10.1103/PhysRev.136.B864
– volume: 50
  start-page: 71
  year: 2020
  ident: 1086_CR43
  publication-title: Annu. Rev. Mater. Res.
  doi: 10.1146/annurev-matsci-070218-010015
– volume: 4
  start-page: 053208
  year: 2016
  ident: 1086_CR24
  publication-title: APL Mater.
  doi: 10.1063/1.4946894
– volume: 5
  start-page: 3187
  year: 2021
  ident: 1086_CR31
  publication-title: Joule
  doi: 10.1016/j.joule.2021.10.010
– volume: 15
  start-page: 5069
  year: 2019
  ident: 1086_CR59
  publication-title: J. Chem. Theory Comput.
  doi: 10.1021/acs.jctc.9b00322
– volume: 7
  start-page: 128
  year: 2021
  ident: 1086_CR33
  publication-title: npj Comput. Mater.
  doi: 10.1038/s41524-021-00596-4
– volume: 9
  start-page: 1668
  year: 2018
  ident: 1086_CR68
  publication-title: J. Phys. Chem. Lett.
  doi: 10.1021/acs.jpclett.8b00124
– volume: 6
  start-page: 700
  year: 2021
  ident: 1086_CR5
  publication-title: Nat. Energy
  doi: 10.1038/s41560-021-00827-4
– volume: 204
  start-page: 111187
  year: 2022
  ident: 1086_CR55
  publication-title: Comput. Mater. Sci.
  doi: 10.1016/j.commatsci.2022.111187
– volume: 152
  start-page: 60
  year: 2018
  ident: 1086_CR39
  publication-title: Comput. Mater. Sci.
  doi: 10.1016/j.commatsci.2018.05.018
– volume: 183
  start-page: 2063
  year: 2012
  ident: 1086_CR10
  publication-title: Comput. Phys. Commun.
  doi: 10.1016/j.cpc.2012.05.008
– volume: 143
  start-page: 12766
  year: 2021
  ident: 1086_CR32
  publication-title: J. Am. Chem. Soc.
  doi: 10.1021/jacs.1c05441
– ident: 1086_CR46
  doi: 10.1145/2939672.2939785
– volume: 9
  start-page: e1411
  year: 2019
  ident: 1086_CR11
  publication-title: WIREs Comput. Mol. Sci.
  doi: 10.1002/wcms.1411
– volume: 1
  start-page: 011002
  year: 2013
  ident: 1086_CR16
  publication-title: APL Mater.
  doi: 10.1063/1.4812323
– volume: 66
  start-page: 1973
  year: 2021
  ident: 1086_CR38
  publication-title: Sci. Bull.
  doi: 10.1016/j.scib.2021.06.011
– volume: 59
  start-page: 102781
  year: 2023
  ident: 1086_CR65
  publication-title: Energy Stor. Mater.
– volume: 6
  year: 2019
  ident: 1086_CR67
  publication-title: Sci. Data
  doi: 10.1038/s41597-019-0097-3
– volume: 6
  start-page: 655
  year: 2021
  ident: 1086_CR35
  publication-title: Nat. Rev. Mater.
  doi: 10.1038/s41578-020-00255-y
– volume: 12
  start-page: 2825
  year: 2011
  ident: 1086_CR45
  publication-title: J. Mach. Learn. Res.
– volume: 89
  start-page: 094104
  year: 2014
  ident: 1086_CR48
  publication-title: Phys. Rev. B
  doi: 10.1103/PhysRevB.89.094104
– volume: 121
  start-page: 6002
  year: 2017
  ident: 1086_CR61
  publication-title: J. Phys. Chem. C.
  doi: 10.1021/acs.jpcc.7b00810
– volume: 13
  year: 2022
  ident: 1086_CR63
  publication-title: Nat. Commun.
  doi: 10.1038/s41467-022-29904-2
– volume: 7
  year: 2020
  ident: 1086_CR17
  publication-title: Sci. Data
  doi: 10.1038/s41597-020-0474-y
– volume: 43
  start-page: 676
  year: 2018
  ident: 1086_CR22
  publication-title: MRS Bull.
  doi: 10.1557/mrs.2018.208
– volume: 4
  start-page: 1541
  year: 2019
  ident: 1086_CR52
  publication-title: ACS Energy Lett.
  doi: 10.1021/acsenergylett.9b00549
– volume: 54
  start-page: 11169
  year: 1996
  ident: 1086_CR6
  publication-title: Phys. Rev. B
  doi: 10.1103/PhysRevB.54.11169
– ident: 1086_CR20
  doi: 10.1007/s11837-014-0888-0
– volume: 5
  year: 2018
  ident: 1086_CR69
  publication-title: Sci. Data
  doi: 10.1038/sdata.2018.65
– volume: 7
  start-page: 275
  year: 2021
  ident: 1086_CR1
  publication-title: Nat. Rev. Mater.
  doi: 10.1038/s41578-021-00376-y
– volume: 934
  start-page: 1
  year: 2021
  ident: 1086_CR27
  publication-title: Phys. Rep.
  doi: 10.1016/j.physrep.2021.08.002
– ident: 1086_CR47
– volume: 6
  start-page: 138
  year: 2020
  ident: 1086_CR40
  publication-title: npj Comput. Mater.
  doi: 10.1038/s41524-020-00406-3
– volume: 43
  start-page: 042101
  year: 2022
  ident: 1086_CR12
  publication-title: J. Semicond.
  doi: 10.1088/1674-4926/43/4/042101
– volume: 71
  start-page: 56
  year: 2022
  ident: 1086_CR28
  publication-title: J. Energy Chem.
  doi: 10.1016/j.jechem.2022.03.052
– volume: 271
  start-page: 108171
  year: 2022
  ident: 1086_CR7
  publication-title: Comput. Phys. Commun.
  doi: 10.1016/j.cpc.2021.108171
– volume: 30
  start-page: 2003087
  year: 2020
  ident: 1086_CR71
  publication-title: Adv. Funct. Mater.
  doi: 10.1002/adfm.202003087
– volume: 6
  start-page: 1036
  year: 2021
  ident: 1086_CR54
  publication-title: Nat. Rev. Mater.
  doi: 10.1038/s41578-021-00345-5
– volume: 32
  start-page: 915
  year: 2020
  ident: 1086_CR62
  publication-title: Chem. Mater.
  doi: 10.1021/acs.chemmater.9b04066
– volume: 7
  year: 2020
  ident: 1086_CR18
  publication-title: Sci. Data
  doi: 10.1038/s41597-020-00723-8
– volume: 81
  start-page: 105665
  year: 2021
  ident: 1086_CR49
  publication-title: Nano Energy
  doi: 10.1016/j.nanoen.2020.105665
– volume: 12
  start-page: 45023
  year: 2020
  ident: 1086_CR73
  publication-title: ACS Appl. Mater. Interfaces
  doi: 10.1021/acsami.0c15728
– volume: 20
  start-page: 736
  year: 2021
  ident: 1086_CR2
  publication-title: Nat. Mater.
  doi: 10.1038/s41563-021-01013-3
– volume: 18
  start-page: 99
  year: 2014
  ident: 1086_CR19
  publication-title: Curr. Opin. Solid State Mater. Sci.
  doi: 10.1016/j.cossms.2014.02.003
– volume: 14
  start-page: 3965
  year: 2021
  ident: 1086_CR29
  publication-title: Energy Environ. Sci.
  doi: 10.1039/D1EE01170G
– volume: 21
  start-page: 395502
  year: 2009
  ident: 1086_CR8
  publication-title: J. Phys. Condens. Matter
  doi: 10.1088/0953-8984/21/39/395502
– volume: 89
  start-page: 106337
  year: 2021
  ident: 1086_CR51
  publication-title: Nano Energy
  doi: 10.1016/j.nanoen.2021.106337
– volume: 5
  start-page: 41
  year: 2019
  ident: 1086_CR23
  publication-title: npj Comput. Mater.
  doi: 10.1038/s41524-019-0173-4
– volume: 35
  start-page: 88
  year: 2021
  ident: 1086_CR30
  publication-title: Energy Stor. Mater.
– volume: 12
  start-page: 12391
  year: 2021
  ident: 1086_CR34
  publication-title: J. Phys. Chem. Lett.
  doi: 10.1021/acs.jpclett.1c03526
– volume: 2
  start-page: 1410
  year: 2018
  ident: 1086_CR4
  publication-title: Joule
  doi: 10.1016/j.joule.2018.05.009
– volume: 176
  start-page: 109544
  year: 2020
  ident: 1086_CR41
  publication-title: Comput. Mater. Sci.
  doi: 10.1016/j.commatsci.2020.109544
– volume: 559
  start-page: 547
  year: 2018
  ident: 1086_CR25
  publication-title: Nature
  doi: 10.1038/s41586-018-0337-2
– volume: 39
  start-page: 45
  year: 2021
  ident: 1086_CR60
  publication-title: Energy Stor. Mater.
– volume: 4
  year: 2017
  ident: 1086_CR70
  publication-title: Sci. Data
  doi: 10.1038/sdata.2016.134
– volume: 2
  start-page: 553
  year: 2020
  ident: 1086_CR26
  publication-title: InfoMat
  doi: 10.1002/inf2.12094
– volume: 6
  start-page: 96
  year: 2020
  ident: 1086_CR58
  publication-title: npj Comput. Mater.
  doi: 10.1038/s41524-020-00360-0
– volume: 186
  start-page: 110064
  year: 2021
  ident: 1086_CR37
  publication-title: Comput. Mater. Sci.
  doi: 10.1016/j.commatsci.2020.110064
– volume: 6
  start-page: 227
  year: 2021
  ident: 1086_CR56
  publication-title: Nat. Energy
  doi: 10.1038/s41560-020-00759-5
– volume: 267
  start-page: 108033
  year: 2021
  ident: 1086_CR13
  publication-title: Comput. Phys. Commun.
  doi: 10.1016/j.cpc.2021.108033
– volume: 140
  start-page: A1133
  year: 1965
  ident: 1086_CR15
  publication-title: Phys. Rev.
  doi: 10.1103/PhysRev.140.A1133
– volume: 20
  start-page: 728
  year: 2021
  ident: 1086_CR3
  publication-title: Nat. Mater.
  doi: 10.1038/s41563-021-01015-1
– volume: 8
  start-page: 250
  year: 2022
  ident: 1086_CR42
  publication-title: npj Comput. Mater.
  doi: 10.1038/s41524-022-00935-z
– volume: 4
  start-page: 319
  year: 2022
  ident: 1086_CR64
  publication-title: Nat. Rev. Phys.
  doi: 10.1038/s42254-022-00423-9
– volume: 1
  start-page: 15010
  year: 2015
  ident: 1086_CR21
  publication-title: npj Comput. Mater.
  doi: 10.1038/npjcompumats.2015.10
– volume: 13
  start-page: 53388
  year: 2021
  ident: 1086_CR66
  publication-title: ACS Appl. Mater. Interfaces
  doi: 10.1021/acsami.1c10749
– volume: 3
  start-page: 3755
  year: 2020
  ident: 1086_CR53
  publication-title: ACS Appl. Energy Mater.
  doi: 10.1021/acsaem.0c00230
– volume: 604
  start-page: 662
  year: 2022
  ident: 1086_CR36
  publication-title: Nature
  doi: 10.1038/s41586-022-04599-z
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Snippet The development of modern civil industry, energy and information technology is inseparable from the rapid explorations of new materials. However, only a small...
Abstract The development of modern civil industry, energy and information technology is inseparable from the rapid explorations of new materials. However, only...
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639/301/1034/1037
Artificial intelligence
Bulk modulus
Characterization and Evaluation of Materials
Chemistry and Materials Science
Computational Intelligence
Computer science
Conductivity
Data processing
Design
Efficiency
Energy
Energy gap
Experiments
Free energy
Heat of formation
Informatics
Information technology
Interdisciplinary subjects
Ion currents
Machine learning
Materials information
Materials Science
Mathematical and Computational Engineering
Mathematical and Computational Physics
Mathematical Modeling and Industrial Mathematics
Proprietary
Research methodology
Simulation
Theoretical
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Title AlphaMat: a material informatics hub connecting data, features, models and applications
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